Comments on “Assessment of pain and quality of life in patients undergoing cardiac surgery: a cohort study”

First, Viana et al.¹ evaluated postoperative pain and quality of life in patients undergoing median sternotomy (via comparisons in a cohort study). However, while comparing outcomes, it is important to present the clinical relevance of the differences found because the p-value shows only a statistical observation related to an alpha error probability²,³. Classical statistical significance is still the predominant way to analyze cohort studies, but clinical significance analysis has been slowly incorporated into the analysis of health-related studies. Statistical significance does not assure that the results are clinically relevant. The dichotomy that emerged from hypothesis testing⁴, namely, the decision to accept or reject the null hypothesis based on the predetermined levels of probability⁵ does not provide any insights into whether the results of the study are important for patients, clinicians, or decision-makers, limiting the value of the tests in the world of evidence-based practice⁴,⁶,⁷. It can be solved by adding the effect size to the significant values (p≤0.05)⁸ or the minimal clinically important difference⁹ of the instruments: Visual Analog Scale (VAS)¹⁰, Brief Pain Inventory (BPI)¹¹, and World Health Organization Quality of Life Questionnaire (WHOQOL)¹². These adjustments facilitate probabilistic reasoning in the clinical applicability of scientific evidence.

Second, the authors used convenience sampling and suggested further studies with larger samples. A convenience sample is one that is drawn from a source that is easily accessible to study. This sample, nonetheless, may not be representative of the population at large; e.g., a convenience sample of students can be drawn from a nearby medical college, but these students may not be representative of all students, such as students in other professional and nonprofessional colleges¹³. According to Andrade¹⁴, the sample size for a study needs to be estimated at the time the study is proposed; too large a sample is unnecessary and unethical, and too small a sample is unscientific and also unethical. The necessary sample size can be calculated using software, based on certain assumptions¹⁵-¹⁷. As such, contributing to the authors and helping later studies with sampling, we designed a sample size a priori using G*Power 3.1.9.7.¹⁸. Regarding the difference between two dependent means (matched pairs), we used the following parameters: effect size=0.5, α=0.05, β=0.90, non-centrality parameter δ=3.3166248, critical t=2.0166922, and df=43 (n=44). Regarding the difference between two independent means (two groups), we used the following parameters for prior sample calculations: effect size=0.5, α=0.05, β=0.90, allocation ratio N2/N1=1, non-centrality parameter δ=2.9580399, critical t=1.6559704, and df=138 (n=140), with 70 patients per group.

ACKNOWLEDGMENTS

Coordination for the Improvement of Higher Education Personnel (CAPES); National Council for Scientific and Technological Development (CNPq); São Paulo Research Foundation (FAPESP); Federal University of Maranhão (UFMA); Federal University of São Carlos (UFSCar); Almir Vieira Dibai-Filho, PhD; Mariana Arias Avila, PhD; and Maria de Fátima Pontes-Silva.

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• Funding: This study was partially supported by the Coordination for the Improvement of Higher Education Personnel (CAPES, BR – code 001). Role funding source had no involvement in the study design, collection, analysis, interpretation of data, writing of the report, or in the decision to submit the article for publication.

Publication Dates

• Publication in this collection
  17 July 2023
• Date of issue
  2023

History

• Received
  29 Mar 2023
• Accepted
  25 Apr 2023